Abstract
This study addresses the issues of semantic entanglement, unclear label structure, and insufficient feature representation in few-shot text classification, and proposes an optimization framework based on structured prompts to enhance semantic understanding and task adaptation under low-resource conditions. The framework first uses a pretrained language model to encode the input text and obtain basic semantic representations. It then introduces structured prompts composed of multi-dimensional semantic factors and integrates them with text features through a learnable combination mechanism, which forms task-related representations with clear boundaries in the latent space. To further strengthen the consistency between text representations and label semantics, the method constructs a structured label embedding matrix and employs a cross-space alignment mechanism to ensure stable matching between textual features and label attributes. In addition, the model applies prompt orthogonality constraints and a joint optimization objective to maintain independence across different semantic factors in the prompts, allowing the structured prompts to provide transparent and controllable guidance for classification decisions. Three types of sensitivity experiments, including learning rate sensitivity, prompt length sensitivity, and data scale sensitivity, are designed to evaluate the stability and robustness of the framework under different conditions. Experimental results show that the proposed structured prompt optimization framework effectively alleviates semantic conflicts and label ambiguity in few-shot text classification. It significantly improves performance on accuracy, precision, recall, and AUC, and demonstrates strong cross-task applicability.
Abstract (translated)
这项研究针对少量样本文本分类中的语义纠缠、标签结构不明确以及特征表示不足等问题，提出了一种基于结构化提示词的优化框架。该框架旨在提升在资源匮乏条件下的语义理解和任务适应能力。 具体来说，该框架首先利用预训练的语言模型对输入文本进行编码，并获取基础的语义表示。然后引入由多维语义因素组成的结构化提示词，并通过可学习组合机制将其与文本特征相融合，在潜在空间中形成具有明确边界的任务相关表示。为了进一步增强文本表征和标签语义之间的一致性，该方法构建了一个结构化的标签嵌入矩阵，并采用跨空间对齐机制来确保文本特征和标签属性之间的稳定匹配。 此外，模型还应用了提示词正交约束以及联合优化目标，以保持不同语义因素在提示中的独立性。这使得结构化提示能够为分类决策提供透明且可控的指导。研究设计了三种敏感性实验（学习率敏感性、提示长度敏感性和数据规模敏感性），用以评估该框架在不同条件下的稳定性和鲁棒性。 实验结果显示，所提出的基于结构化提示词的优化框架有效地缓解了少量样本文本分类中的语义冲突和标签模糊问题。它显著提高了准确率、精确度、召回率及AUC指标的表现，并展示了强大的跨任务适用性。
URL
https://arxiv.org/abs/2602.23753
